Search Results for author:
Found 22 papers, 11 papers with code
CLLMs: Consistency Large Language Models
Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation.
Model Extraction Attacks on Split Federated Learning
We show that under practical cases, the proposed ME attacks work exceptionally well for SFL.
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server.
CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction
We explore the sparsity in ViT and observe that informative patches and heads are sufficient for accurate image recognition.
N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores
From the software perspective, we mathematically and systematically model the latency and resource utilization of the proposed heterogeneous accelerator, regarding varying system design configurations.
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
However, it is challenging for crossbar architecture to exploit the sparsity in the DNN.
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery
In this work, we propose RADAR, a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to protect DNN weights against PBFA.
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-Point
Model quantization has emerged as a mandatory technique for efficient inference with advanced Deep Neural Networks (DNN).
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which enables dynamical selection of a subset of filters for more efficient local computation given the data input.
A Progressive Sub-Network Searching Framework for Dynamic Inference
Many techniques have been developed, such as model compression, to make Deep Neural Networks (DNNs) inference more efficiently.
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning
Thus motivated, we propose a new training method called \textit{kernel-wise Soft Mask} (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task, while using the same backbone model.
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
Prior works of BFA focus on un-targeted attack that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory.
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack
Recently, a new paradigm of the adversarial attack on the quantized neural network weights has attracted great attention, namely, the Bit-Flip based adversarial weight attack, aka.
TBT: Targeted Neural Network Attack with Bit Trojan
However, when the attacker activates the trigger by embedding it with any input, the network is forced to classify all inputs to a certain target class.
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?
Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structrued pruning is not competitive in terms of both storage and computation efficiency.
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
In this work, we show that shrinking the model size through proper weight pruning can even be helpful to improve the DNN robustness under adversarial attack.
Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components.
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack
Training the network with Gaussian noise is an effective technique to perform model regularization, thus improving model robustness against input variation.
Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation
In the past years, Deep convolution neural network has achieved great success in many artificial intelligence applications.
Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy
Deep convolution neural network has achieved great success in many artificial intelligence applications.
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Blind pre-processing improves the white box attack accuracy of MNIST from 94. 3\% to 98. 7\%.
Developing All-Skyrmion Spiking Neural Network
In this work, we have proposed a revolutionary neuromorphic computing methodology to implement All-Skyrmion Spiking Neural Network (AS-SNN).
